论文标题
改进了医学成像分类神经网络的可训练校准方法
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
论文作者
论文摘要
最近的作品表明，深层神经网络可以在医学成像域中的各种图像分类任务中实现超人的性能。但是，这些作品主要集中在分类准确性上，而忽略了不确定性量化的重要作用。从经验上讲，神经网络通常在预测中被错误地校准和过度自信。在任何自动决策系统中，这种误解都可能是有问题的，但是我们专注于神经网络误校准有可能导致重大治疗错误的医学领域。我们提出了一种新型的校准方法，该方法可以保持整体分类精度，同时显着改善模型校准。所提出的方法基于预期的校准误差，这是量化错误校准的常见度量。我们的方法可以轻松地将其作为辅助损失项整合到任何分类任务中，因此不需要明确的训练回合进行校准。我们表明，我们的方法可在各种架构和数据集中大大减少校准误差。
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy, ignoring the important role of uncertainty quantification. Empirically, neural networks are often miscalibrated and overconfident in their predictions. This miscalibration could be problematic in any automatic decision-making system, but we focus on the medical field in which neural network miscalibration has the potential to lead to significant treatment errors. We propose a novel calibration approach that maintains the overall classification accuracy while significantly improving model calibration. The proposed approach is based on expected calibration error, which is a common metric for quantifying miscalibration. Our approach can be easily integrated into any classification task as an auxiliary loss term, thus not requiring an explicit training round for calibration. We show that our approach reduces calibration error significantly across various architectures and datasets.
